Data Makes Better Data Scientists
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866910460568141824 |
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| author | Zhao, Jinjin Gal, Avidgor Krishnan, Sanjay |
| author_facet | Zhao, Jinjin Gal, Avidgor Krishnan, Sanjay |
| contents | With the goal of identifying common practices in data science projects, this paper proposes a framework for logging and understanding incremental code executions in Jupyter notebooks. This framework aims to allow reasoning about how insights are generated in data science and extract key observations into best data science practices in the wild. In this paper, we show an early prototype of this framework and ran an experiment to log a machine learning project for 25 undergraduate students. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17690 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Makes Better Data Scientists Zhao, Jinjin Gal, Avidgor Krishnan, Sanjay Human-Computer Interaction With the goal of identifying common practices in data science projects, this paper proposes a framework for logging and understanding incremental code executions in Jupyter notebooks. This framework aims to allow reasoning about how insights are generated in data science and extract key observations into best data science practices in the wild. In this paper, we show an early prototype of this framework and ran an experiment to log a machine learning project for 25 undergraduate students. |
| title | Data Makes Better Data Scientists |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2405.17690 |